This chapter introduces Bayesian probability and the rules of probability theory, emphasizing that Bayesian probability emerges as the uniquely consistent extension of deductive (Boolean) logic to contexts of uncertainty and incomplete information

Here, we dive into the details of RCT in order to contrast it with the ideas encapsulated by BPS. What is rational political behavior? We discuss the basic assumptions of the RCT model, especially regarding the cognitive abilities and motivations that people use when making complex decisions about abstract topics such as public policy preferences and voting. BPS diverges from RCT in two critical ways: it is process-oriented, and incorporates known limits on the cognitive abilities of both citizens and elites into models of political behavior, and 2) it explains motivations behind preferences, especially those distinct from material or economic self-interest. We interrogate whether standard RCT assumptions are plausible, such as whether voters can adequately collect and weigh all the information relevant to a political choice in their heads simultaneously, why citizens turn out to vote when their potential to impact the outcome of an election is infinitesimal, or the public’s willingness to support costly anti-terror policies even if the likelihood of a serious attack is vanishingly small.